Monitoring of posture allocations and activities enables accurate estimation of energy expenditure and may aid in obesity prevention and treatment. At present, accurate devices rely on multiple sensors distributed on the body and thus may be too obtrusive for everyday use. This paper presents a novel wearable sensor which is capable of very accurate recognition of common postures and activities. The patterns of heel acceleration and plantar pressure uniquely characterize postures and typical activities while requiring minimal preprocessing and no feature extraction. The shoe sensor was tested in 9 adults performing sitting and standing postures and while walking, running, stair ascent/descent and cycling. Support Vector Machines were used for classification. A four-fold validation of a 6-class subject-independent group model showed 95.2% average accuracy of posture/activity classification on full sensor set and over 98% on optimized sensor set. Using a combination of acceleration/pressure also enabled a pronounced reduction of the sampling frequency (25Hz to 1Hz) without significant loss of accuracy (98% vs. 93%). Subjects had shoe sizes (US) M9.5-11 and W7-9, and body mass index from 18.1 to 39.4 kg/m2 thus suggesting that the device can be used by individuals with varying anthropometric characteristics.